Evolution of network structure by temporal learning
arXiv:0811.4306 · doi:10.1016/j.physa.2008.12.073
Abstract
We study the effect of learning dynamics on network topology. A network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity. This incorporates necessary competition between different edges. The final network we obtain is robust and has a broad degree distribution.
revised manuscript in communication